AI-200 Multiple Select — 30 Questions

Question 1

When designing an event-driven architecture using Azure Event Grid, which THREE security or delivery mechanisms can be configured on an event subscription? Each correct answer represents a valid configuration option.

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Advanced filtering based on event data property values. Option B is correct because Event Grid event subscriptions support advanced filtering, which evaluates event data fields (such as string, number, boolean, and arrays) against operators like StringContains, NumberGreaterThan, and BoolEquals to route only matching events. Option C is correct because an event subscription can define a dead-letter destination, which must be an Azure Blob Storage container, where undeliverable events are stored along with a reason for delivery failure. Option E is correct because Event Grid supports using a managed identity (system-assigned or user-assigned) on the event subscription to authenticate delivery to supported destinations such as webhooks and Service Bus, avoiding embedded secrets. Option A is not applicable because AMQP 1.0 broker transaction coordination is a Service Bus/AMQP messaging concept, not an Event Grid subscription setting. Option D is not applicable because Service Bus session lock duration is a Service Bus queue/subscription feature and cannot be configured on an Event Grid event subscription.

Answer

AMQP 1.0 broker transaction coordination

Answer

Service Bus session lock duration extension

Question 2

You are auditing your Azure subscription for security best practices related to Azure AI resources. Which TWO of the following actions help enforce governance and security? (Choose two)

Accepted Answer

Use Azure Policy to enforce allowed deployment regions and mandatory tagging.. Applying resource locks prevents accidental deletion, and enforcing Azure Policy ensures regional and configuration compliance.

Answer

Grant every user Contributor rights to the entire Azure subscription.

Answer

Share the master subscription co-administrator password with all external contractors.

Answer

Disable all firewall rules on Azure Key Vault.

Question 3

You are designing an enterprise cloud solution that integrates Azure Event Grid, Azure Service Bus, and Azure Event Hubs. You need to select the appropriate service for three specific event-driven scenarios. Which THREE pairings of Azure messaging service to scenario are correct? (Choose three)

Accepted Answer

Azure Service Bus for managing financial transactions requiring strict ordering, sessions, and dead-lettering.. Option A is correct because Azure Service Bus is the enterprise brokered-messaging service that natively supports FIFO ordering via sessions, message sessions for correlated/ordered processing, and built-in dead-letter queues for poison messages, which are exactly the requirements for financial transactions. Option B is correct because Azure Event Hubs is designed for massive-scale event ingestion (millions of events per second, with throughput units or processing units) and is the right fit for 500,000 telemetry events per second from smart grid sensors. Option C is correct because Azure Event Grid is a reactive, push-based event routing service that natively emits blob-created events from Azure Storage and can deliver them to serverless endpoints such as Azure Functions for AI processing. Option D is not correct because Event Hubs is a high-throughput streaming ingestion service, not a low-latency pub/sub webhook trigger for discrete user profile updates; Event Grid is the appropriate service for that scenario. Option E is not correct because Event Grid is not built for continuous high-volume telemetry streaming into analytics engines like Databricks; Event Hubs (often with Capture or Stream Analytics) is the correct ingestion path for that workload.

Answer

Azure Event Hubs for triggering real-time serverless webhooks upon user profile updates.

Answer

Azure Event Grid for streaming continuous IoT sensor telemetry into Azure Databricks.

Question 4

You are designing an enterprise event-driven architecture for AI workloads. Which TWO Azure services provide native support for publishing discrete, reactive event notifications when resources are created or modified? Each correct answer represents a complete solution.

Accepted Answer

Azure Blob Storage (native event notifications). Azure Blob Storage natively supports event notifications that fire discrete, reactive events such as Microsoft.Storage.BlobCreated and Microsoft.Storage.BlobDeleted when blobs are created or modified, which fits the requirement for resource-change notifications. Azure Event Grid is a fully managed publish-subscribe eventing service that natively delivers discrete, reactive event notifications from Azure resources (including Blob Storage) to subscribers via event subscriptions, making it the canonical service for this scenario. Azure Notification Hubs is a push-notification service for mobile/consumer devices (APNs, FCM, WNS), not a resource-event publisher. Azure Event Hubs is a big-data streaming/telemetry ingestion service (high-throughput event streaming), not a discrete resource-change notification publisher. Azure Service Bus Queues are a brokered messaging construct for commands/messages with queuing semantics, not native resource event notifications.

Answer

Azure Notification Hubs

Answer

Azure Event Hubs

Answer

Azure Service Bus Queues

Question 5

You are architecting an asynchronous machine learning inference pipeline. Which TWO Azure services allow you to decouple event publishers from event consumers using a publish-subscribe (pub/sub) pattern? Each correct answer represents a valid pub/sub architecture service.

Accepted Answer

Azure Event Grid. Azure Event Grid (A) is correct because it is a fully managed event-routing service that implements the pub/sub pattern: publishers emit events to topics and multiple subscribers receive them via event subscriptions with filters, enabling full decoupling of producers and consumers. Azure Service Bus Topics (E) is correct because a topic supports the publish-subscribe model where each subscriber creates its own subscription and receives a copy of every message, providing decoupled, asynchronous delivery with features like filters and sessions. Azure Notification Hubs (B) is a push-notification broadcast service for mobile devices, not a general event pub/sub backbone for decoupling publishers and consumers. Azure Storage Queues (C) implements point-to-point queue messaging where each message is consumed by a single receiver, not a fan-out pub/sub pattern. Azure Event Hubs Capture (D) is a feature that automatically persists streaming data to Blob Storage or Data Lake, not a pub/sub decoupling service.

Answer

Azure Notification Hubs

Answer

Azure Storage Queues

Answer

Azure Event Hubs Capture

Question 6

Your AI processing pipeline requires high-throughput streaming ingestion and retention of telemetry data. Which THREE features or capabilities are native to Azure Event Hubs? Each correct answer represents a complete capability.

Accepted Answer

Event Hubs Capture for automatically batching and saving stream data to Azure Storage. Option A is correct because Event Hubs Capture is a native feature that automatically batches streaming data and writes it to Azure Blob Storage or Azure Data Lake Storage (in Avro format) based on a time or size window, satisfying the retention requirement without custom code. Option C is correct because Consumer Groups are a native Event Hubs construct that lets multiple independent applications each maintain their own offset and read the same event stream independently, which is essential for multiple downstream consumers. Option D is correct because Event Hubs partitions the stream and allows multiple receivers to process partitions in parallel, which is the foundation of its high-throughput streaming ingestion and parallel processing model. Option B is not correct because Event Hubs does not natively push events via webhooks for serverless triggers; that pattern belongs to Event Grid, whereas Event Hubs uses AMQP/Kafka pull-based consumption. Option E is not correct because automatic dead-lettering based on a MaxDeliveryCount threshold is a Service Bus queue/subscription feature, not a native Event Hubs capability.

Answer

Native webhook push delivery for discrete serverless event triggers

Answer

Automatic dead-lettering of messages exceeding a MaxDeliveryCount threshold

Question 7

Your team is choosing between using Azure AI Search or Cosmos DB for NoSQL for a new project. Which TWO factors are key considerations that favor Azure AI Search?

Accepted Answer

Need for deep integration with full-text search (BM25) and hybrid search.. Option A is correct because Azure AI Search is purpose-built as a search engine that natively supports full-text search using the BM25 ranking algorithm as well as hybrid search that combines keyword and vector queries, which Cosmos DB for NoSQL does not provide out of the box. Option D is correct because Azure AI Search includes built-in semantic re-ranking (semantic ranker/L2 reranking) that reorders results using deep-learning models to improve relevance and accuracy, a capability Cosmos DB for NoSQL lacks natively. Option B is not a factor favoring Azure AI Search, since petabyte-scale storage is a strength of Cosmos DB for NoSQL rather than a search service. Option C is not a factor favoring Azure AI Search, because high-frequency transactional ACID updates to metadata are handled by Cosmos DB for NoSQL's transactional capabilities, not by a search index. Option E is not a factor favoring Azure AI Search, as lowest possible database-level latency is a characteristic of Cosmos DB for NoSQL's globally distributed, low-latency data plane rather than the search service.

Answer

Requirement to store petabytes of data.

Answer

Requirement for high-frequency transactional ACID updates to metadata.

Answer

Need for lowest possible latency at the database level.

Question 8

You are designing an operational dashboard in Azure Monitor. Which TWO visualization components can you include in an Azure Workbook? (Choose two)

Accepted Answer

KQL query result grids and charts. Option B is correct because Azure Workbooks natively render KQL query results as grids, charts, and other visualizations, which is the core mechanism for displaying Log Analytics data in a workbook. Option E is correct because Workbooks can also visualize Azure Monitor platform metrics directly through metric chart components, letting you combine metrics and logs in one dashboard. Options A, C, and D are incorrect because DDL scripts, Key Vault plain-text secrets, and Docker image binary layers are not visualization components and cannot be embedded as workbook tiles; Workbooks only display query/metric-based visualizations, text, links, and parameters.

Answer

Azure SQL database table creation scripts (DDL)

Answer

Azure Key Vault plain-text master password files

Answer

Docker container image binary layers

Question 9

You are optimizing an enterprise Azure Cosmos DB for NoSQL vector database. Which THREE administrative or architectural practices help ensure high availability and predictable performance? (Choose three)

Accepted Answer

Choose a partition key that evenly distributes data and query load across physical partitions. Option A is correct because a well-chosen partition key that evenly distributes data and query load across physical partitions prevents hot partitions, which is essential for predictable performance and scalability in Cosmos DB for NoSQL. Option B is correct because enabling multi-region write replication (multi-master) provides global low-latency writes and improves availability by allowing the account to continue serving requests even if a region becomes unavailable. Option E is correct because provisioning adequate Request Units (RUs) or configuring autoscale throughput ensures the database has enough throughput capacity to handle workload demands, preventing throttling (429 errors) and maintaining predictable performance. Option C is incorrect because storing all documents in a single logical partition without a partition key creates a hot partition and limits scalability, harming both availability and performance. Option D is incorrect because disabling automatic indexing entirely removes the default indexing policy that supports efficient queries, which would degrade query performance rather than improve it.

Answer

Store all documents in a single partition without a partition key

Answer

Disable automatic indexing entirely across all properties

Question 10

When configuring an Azure AI Search vector profile, which TWO elements must be successfully referenced together to make the profile functional? (Choose two)

Accepted Answer

An algorithm configuration name. Option B is correct because a vector profile in Azure AI Search must reference a valid algorithm configuration name (the HNSW or exhaustive KNN configuration defined in the index's vectorSearch.algorithms collection) that determines how vector similarity is computed during queries. Option C is correct because when integrated vectorization is used, the vector profile must also reference a vectorizer name (defined in the vectorSearch.vectorizers collection) so the search service can convert text queries into embeddings at query time. These two references — algorithm configuration and vectorizer — are the required components that make a vector profile functional. Option A is incorrect because a Cognitive Services storage blob container is not part of a vector profile definition; blob containers are used as data sources for indexers, not for vector profiles. Option D is incorrect because an Azure SQL connection string belongs to a data source definition for indexers, not to a vector profile. Option E is incorrect because Cosmos DB RU/s throughput is a database provisioning setting and has no role in defining a vector profile.

Answer

A Cognitive Services storage blob container

Answer

An Azure SQL connection string

Answer

A Cosmos DB throughput RU/s setting

Question 11

Which THREE logs or telemetry types should you collect in Azure Monitor to comprehensively troubleshoot an AI-powered web application?

Accepted Answer

Application requests and response status codes.. Application Insights collects request telemetry, dependency tracking for API calls, and trace logs for debugging logic.

Answer

Azure Bastion audit logs.

Answer

Azure Firewall packet capture PCAP files.

Question 12

You are troubleshooting an Azure Function triggered by an Azure Event Grid subscription. The function is failing to receive events. Which TWO diagnostic steps should you take in the Azure Portal or via Azure CLI? Each correct answer is a valid troubleshooting action.

Accepted Answer

Inspect the configured dead-letter blob storage container for undelivered event files.. Option A is correct because Event Grid supports a dead-letter destination (a Storage blob container) where events are written when they cannot be delivered to the subscriber, so inspecting that container reveals undelivered event files and their failure reasons. Option C is correct because Event Grid exposes metrics such as Delivery Failed Events, Dropped Events, and Matched Events in Azure Monitor, which directly show whether events are being matched to the subscription and whether delivery to the function is failing. Option B is not applicable because AMQP port 5671 is used by Service Bus and Event Hubs, not by Event Grid, which delivers events over HTTPS (port 443) to webhook/function endpoints. Option D is not applicable because Service Bus session lock renewal is a Service Bus feature unrelated to Event Grid subscriptions. Option E is not applicable because Event Hubs consumer group checkpoints relate to Event Hubs consumption, not to Event Grid event delivery to an Azure Function.

Answer

Verify the AMQP listener port 5671 firewall rules on the function app.

Answer

Review Service Bus session lock renewal logs.

Answer

Check Event Hubs consumer group checkpoint offset status.

Question 13

You are configuring secure ingress and network isolation for an Azure Container Apps environment. Which THREE capabilities are supported when configuring networking for Container Apps? (Choose three.)

Accepted Answer

Exposing the environment with either external (public) or internal (private VNet) ingress. Option A is correct because Azure Container Apps environments support both external ingress, which exposes apps to the public internet via a public endpoint, and internal ingress, which restricts access to a private VNet or the environment's internal network. Option C is correct because you can deploy a Container Apps environment into a customer-managed Azure Virtual Network subnet, enabling network isolation, private endpoints, and control over inbound/outbound traffic. Option E is correct because Container Apps supports custom domain names with automated managed TLS/SSL certificates, including free managed certificates for custom domains and binding of existing certificates. Option B is incorrect because customers cannot directly modify physical Azure datacenter top-of-rack switches; Azure manages the physical network infrastructure. Option D is incorrect because attaching physical Fibre Channel SAN arrays is not a supported Container Apps networking capability; persistent storage in Container Apps is provided through Azure Files or managed storage abstractions, not direct SAN attachment.

Answer

Direct modification of physical Azure datacenter top-of-rack switches

Answer

Direct attachment of physical Fibre Channel storage SAN arrays

Question 14

You are designing a high-performance RAG data layer architecture using Azure AI Search. Which THREE indexing and retrieval strategies should you implement to maximize recall and precision? (Choose three)

Accepted Answer

Enable semantic ranker to re-order top results using deep learning models. Option A is correct because Azure AI Search's semantic ranker applies deep-learning language models (Microsoft's semantic re-ranking) to re-order the top BM25/vector results, improving precision by promoting semantically relevant documents that lexical or vector scoring alone may rank lower. Option C is correct because HNSW (Hierarchical Navigable Small World) is the approximate nearest neighbor algorithm used in Azure AI Search vector indexes, and tuning m (number of bi-directional links per node), efConstruction (index-time candidate list size), and efSearch (query-time candidate list size) directly controls the recall-versus-latency tradeoff — higher values increase recall at the cost of latency. Option E is correct because hybrid search fuses BM25 keyword scoring with vector similarity (via Reciprocal Rank Fusion in Azure AI Search), capturing both exact term matches and semantic similarity, which raises recall and precision versus either method alone. Option B is incorrect because disabling scoring and ranking would eliminate relevance ordering entirely, destroying precision rather than maximizing it. Option D is incorrect because embeddings must be stored in vector fields (Collection(Edm.Single) with a vector profile and HNSW configuration) to be searchable; storing them as plain text in unindexed fields makes them unusable for vector retrieval.

Answer

Disable all scoring and ranking algorithms to save CPU cycles

Answer

Store embeddings as plain text strings in unindexed fields

Question 15

When configuring an Azure AI Search vector index, which TWO parameters can be adjusted within the HNSW algorithm configuration to trade off between search speed, memory usage, and recall accuracy? (Choose two)

Accepted Answer

efSearch. Option A, efSearch, is correct because it sets the size of the dynamic candidate list used during query time in HNSW; increasing efSearch explores more neighbors, improving recall accuracy at the cost of slower searches, while decreasing it speeds up queries with lower recall. Option E, m, is correct because it defines the number of bidirectional links created for each node in the HNSW graph; a higher m improves recall and accuracy but consumes more memory, whereas a lower m reduces memory usage and speeds up indexing/search with reduced recall. Option B, replicaCount, is incorrect because it controls the number of replicas for high availability and query throughput in Azure AI Search, not the HNSW speed/memory/recall tradeoff. Option C, partitionCount, is incorrect because it controls index partitioning for storage and write scaling, not HNSW tuning. Option D, maxTextLength, is incorrect because it is a field/analyzer-related setting for text content length, unrelated to HNSW graph parameters.

Answer

replicaCount

Answer

partitionCount

Answer

maxTextLength

Question 16

When designing an Azure AI Search index schema, which TWO properties must be defined for every field in the index? (Choose two)

Accepted Answer

name. Option B (name) is correct because every field in an Azure AI Search index schema requires a unique name that identifies the field within the fields collection, and this name is used in queries, filters, and result documents. Option E (type) is correct because each field must declare an Edm data type (such as Edm.String, Edm.Int32, Edm.Boolean, Edm.DateTimeOffset, or Collection(Edm.String)) so the search engine knows how to index, store, and query the field's values. Option A (analyzerName) is not required for every field; analyzers apply only to searchable string fields and default to the standard Lucene analyzer when omitted. Option C (scoringProfile) is not a field-level property at all; scoring profiles are defined at the index level to influence relevance ranking. Option D (vectorDimensions) is only required for fields of type Collection(Edm.Single) that participate in vector search, not for every field.

Answer

analyzerName

Answer

scoringProfile

Answer

vectorDimensions

Question 17

You are troubleshooting a containerized AI model deployment in Azure Container Apps that fails to pass its HTTP readiness probe. You need to inspect the container logs and runtime diagnostics. Which THREE tools or commands can you use to troubleshoot this issue? Each correct answer presents a complete solution.

Accepted Answer

Access the Azure Portal and use the Console diagnostic tool for the specific container app revision.. Option A is correct because the Azure Portal's Console diagnostic tool for a specific container app revision opens an interactive shell inside the running container, letting you inspect the filesystem, processes, and probe-related runtime behavior directly. Option B is correct because 'az containerapp logs show' retrieves the system and console log streams for a container app (with parameters like --name, --resource-group, and --revision), which is exactly what you need to see why the HTTP readiness probe is failing. Option C is correct because Container Apps environments can send console and system logs to a Log Analytics workspace, where you can query them with KQL (for example, the ContainerAppConsoleLogs_CL and ContainerAppSystemLogs_CL tables) to diagnose probe failures. Option D is not correct because 'az aks browse' targets Azure Kubernetes Service clusters, not Azure Container Apps, and it opens a Kubernetes dashboard rather than exposing container logs. Option E is not correct because 'kubectl describe pod' works against a Kubernetes API server and pods, whereas Container Apps is a serverless platform that abstracts away pods and does not expose them to kubectl.

Answer

Execute az aks browse to open the Kubernetes dashboard.

Answer

Use kubectl describe pod to inspect Kubernetes events.

Question 18

Your development team is planning to deploy a containerized natural language processing model to Azure Container Apps. Which TWO features are natively supported by Azure Container Apps for managing application lifecycle and traffic? (Choose two.)

Accepted Answer

Built-in KEDA-based autoscaling including scaling to zero. Azure Container Apps natively supports KEDA-based autoscaling, including scale-to-zero, which is option B — this is a core platform capability that lets containerized workloads (such as an NLP model) scale based on event sources like HTTP traffic or queues without managing Kubernetes infrastructure. Option C is also correct because Azure Container Apps provides native revision management, allowing immutable revisions of an app to be created and traffic splitting (for example, weighted percentages) between revisions for blue-green or canary deployments. Option A is incorrect because Azure Container Apps is a serverless container platform and does not deploy or expose bare-metal hypervisors to users. Option D is incorrect because Azure Container Apps does not provide direct root SSH access to individual container instances; debugging is done via logs, console, or exec into containers without root host access. Option E is incorrect because the Kubernetes control plane is fully managed by Azure and users cannot manually configure master nodes.

Answer

Automatic deployment of bare-metal hypervisors

Answer

Direct root SSH access to individual container instances for interactive debugging

Answer

Direct manual configuration of control plane Kubernetes master nodes

Question 19

Your enterprise application uses Azure Event Grid. Which TWO event schemas are natively supported when publishing events to an Event Grid custom topic? (Choose two)

Accepted Answer

CloudEvents 1.0 schema. Azure Event Grid custom topics natively support two event schemas for publishing: the CloudEvents 1.0 schema (option D) and the proprietary Azure Event Grid schema (option E). CloudEvents 1.0 is the CNCF-standardized, interoperable envelope format that Event Grid accepts directly, while the Azure Event Grid schema is the service's original native format with fields like 'eventType', 'subject', 'data', and 'dataVersion'. Both are selectable via the topic's input schema setting and are the only two schemas Event Grid accepts for custom topic publishing. Options A, B, and C are incorrect: Apache Kafka record batch binary, AMQP 1.0 binary stream envelopes, and SOAP 1.2 XML are not supported input schemas for Event Grid custom topics, even though Event Grid can deliver to Kafka or use AMQP internally for its own transport.

Answer

Apache Kafka record batch binary schema

Answer

AMQP 1.0 binary stream envelope

Answer

SOAP 1.2 XML messaging schema

Question 20

When troubleshooting a Python AI application integrated with Application Insights, you want to ensure comprehensive error tracking and telemetry collection. Which TWO of the following SDK practices should you implement? (Choose two)

Accepted Answer

Wrap unhandled exception handling with telemetry_client.track_exception() to capture stack traces.. Option B is correct because telemetry_client.track_exception() explicitly records exception details, including the exception type, message, and stack trace, into Application Insights, which is essential for diagnosing unhandled errors in a Python AI application. Option D is correct because the Application Insights SDK buffers telemetry and sends it asynchronously; calling telemetry_client.flush() before the process exits ensures buffered items are transmitted rather than lost, which is critical in short-lived or containerized workloads. Option A is wrong because disabling the telemetry client in production eliminates the error tracking and telemetry collection the scenario requires. Option C is wrong because routing logs only to local text files on ephemeral container storage means they are lost when the container is recycled and never reach Application Insights. Option E is wrong because storing plain-text passwords in custom dimensions exposes secrets in telemetry data and violates security best practices.

Answer

Disable the Application Insights telemetry client entirely in production to save bandwidth.

Answer

Route all application logs exclusively to local text files on ephemeral container storage.

Answer

Store plain-text passwords in telemetry custom dimensions.